HS project

Zoom in immune cells

This file is used to generate a dataset containing only immune cells.

library(dplyr)
library(patchwork)
library(ggplot2)

.libPaths()

## [1] "/usr/local/lib/R/library"

Preparation

In this section, we set the global settings of the analysis. We will store data there :

save_name = "immune_cells"
out_dir = "."

We load the sample information :

sample_info = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_sample_info.rds"))
project_names_oi = sample_info$project_name

graphics::pie(rep(1, nrow(sample_info)),
              col = sample_info$color,
              labels = sample_info$project_name)

Here are custom colors for each cell type :

color_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_color_markers.rds"))

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

Make immune_cells dataset

Atlas

We load the combined dataset containing all cell types from all samples :

sobj = readRDS(paste0(out_dir, "/../../3_combined/hs_hd_sobj.rds"))
sobj

## An object of class Seurat 
## 20003 features across 12111 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

We represent cells in the tSNE :

name2D = "harmony_38_tsne"

We smooth cell type annotation at a cluster level :

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]

We look gene markers expression level, cell annotation and cluster-smoothed annotation on the projection, to locate immune_cells cells :

immune_markers = c("PTPRC", "AIF1", "CD3E")
immune_cell_type = c("CD4 T cells", "CD8 T cells",
                     "Langerhans cells", "macrophages", "B cells")
color_markers[!(names(color_markers) %in% immune_cell_type)] = "gray92"

# Feature Plot
plot_list = lapply(immune_markers, FUN = function(one_gene) {
  p = Seurat::FeaturePlot(sobj, reduction = name2D,
                          features = one_gene) +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.subtitle = element_text(hjust = 0.5))
  return(p)
})

# Cell type annotation
plot_list[[length(plot_list) + 1]] = Seurat::DimPlot(sobj, group.by = "cell_type",
                                                     cols = color_markers, reduction = name2D,
                                                     order = save_name) +
  ggplot2::labs(title = "Cell annotation",
                subtitle = paste0(sum(sobj$cell_type %in% immune_cell_type),
                                  " cells")) +
  Seurat::NoAxes() + Seurat::NoLegend() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 plot.subtitle = element_text(hjust = 0.5))

# Cluster-smoothed annotation
plot_list[[length(plot_list) + 1]] = Seurat::DimPlot(sobj,
                                                     reduction = name2D,
                                                     group.by = "cluster_type") +
  ggplot2::scale_color_manual(values = c(unname(unlist(color_markers[immune_cell_type])),
                                         rep("gray92", length(color_markers) - length(immune_cell_type))),
                              breaks = c(immune_cell_type, setdiff(names(color_markers), immune_cell_type))) +
  ggplot2::labs(title = "Cluster annotation",
                subtitle = paste0(sum(sobj$cluster_type %in% immune_cell_type),
                                  " cells")) +
  Seurat::NoAxes() + Seurat::NoLegend() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 plot.subtitle = element_text(hjust = 0.5))

patchwork::wrap_plots(plot_list, nrow = 1)

Combined dataset

We extract cells of interest based on the clustering :

sobj = subset(sobj, cluster_type %in% immune_cell_type)
sobj

## An object of class Seurat 
## 20003 features across 2329 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

We remove all things that were calculated based on the full atlas :

sobj = Seurat::DietSeurat(sobj)
sobj

## An object of class Seurat 
## 20003 features across 2329 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)

Clean metadata

We keep a subset of meta.data and reset levels :

sobj@meta.data = sobj@meta.data[, c("orig.ident", "nCount_RNA", "nFeature_RNA", "log_nCount_RNA",
                                    "project_name", "sample_identifier", "sample_type",
                                    "laboratory", "location", "Seurat.Phase", "cyclone.Phase",
                                    "percent.mt", "percent.rb", "cell_type")]

sobj$orig.ident = factor(sobj$orig.ident, levels = levels(sample_info$project_name))
sobj$project_name = factor(sobj$project_name, levels = levels(sample_info$project_name))
sobj$sample_identifier = factor(sobj$sample_identifier, levels = levels(sample_info$sample_identifier))
sobj$sample_type = factor(sobj$sample_type, levels = levels(sample_info$sample_type))

summary(sobj@meta.data)

##    orig.ident    nCount_RNA     nFeature_RNA  log_nCount_RNA    project_name
##  2021_31:269   Min.   :  745   Min.   : 500   Min.   : 6.629   2021_31:269  
##  2021_36:196   1st Qu.: 2692   1st Qu.:1187   1st Qu.: 7.898   2021_36:196  
##  2021_41:359   Median : 3932   Median :1574   Median : 8.277   2021_41:359  
##  2022_03:914   Mean   : 6218   Mean   :1922   Mean   : 8.421   2022_03:914  
##  2022_14:248   3rd Qu.: 7736   3rd Qu.:2434   3rd Qu.: 8.954   2022_14:248  
##  2022_01: 85   Max.   :47738   Max.   :6182   Max.   :10.774   2022_01: 85  
##  2022_02:258                                                   2022_02:258  
##  sample_identifier sample_type  laboratory          location        
##  HS_1:269          HS:1986     Length:2329        Length:2329       
##  HS_2:196          HD: 343     Class :character   Class :character  
##  HS_3:359                      Mode  :character   Mode  :character  
##  HS_4:914                                                           
##  HS_5:248                                                           
##  HD_1: 85                                                           
##  HD_2:258                                                           
##  Seurat.Phase       cyclone.Phase        percent.mt         percent.rb     
##  Length:2329        Length:2329        Min.   : 0.03017   Min.   : 0.9857  
##  Class :character   Class :character   1st Qu.: 2.55565   1st Qu.:14.0835  
##  Mode  :character   Mode  :character   Median : 3.81070   Median :18.2761  
##                                        Mean   : 4.49685   Mean   :17.9787  
##                                        3rd Qu.: 5.57069   3rd Qu.:21.9886  
##                                        Max.   :19.58763   Max.   :45.7462  
##                                                                            
##             cell_type  
##  CD4 T cells     :884  
##  CD8 T cells     :597  
##  macrophages     :475  
##  Langerhans cells:286  
##  B cells         : 45  
##  proliferative   : 23  
##  (Other)         : 19

Processing

Metadata

How many cells by sample ?

table(sobj$project_name)

## 
## 2021_31 2021_36 2021_41 2022_03 2022_14 2022_01 2022_02 
##     269     196     359     914     248      85     258

We represent this information as a barplot :

aquarius::plot_barplot(df = table(sobj$project_name,
                                  sobj$cell_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("project_name", "cell_type", "nb_cells")),
                       x = "project_name", y = "nb_cells", fill = "cell_type",
                       position = position_stack()) +
  ggplot2::scale_fill_manual(values = color_markers,
                             breaks = names(color_markers),
                             name = "Cell type")

Projection

We remove genes that are expressed in less than 5 cells :

sobj = aquarius::filter_features(sobj, min_cells = 5)

## [1] 20003  2329
## [1] 15121  2329

sobj

## An object of class Seurat 
## 15121 features across 2329 samples within 1 assay 
## Active assay: RNA (15121 features, 1580 variable features)

We normalize the count matrix for remaining cells :

sobj = Seurat::NormalizeData(sobj,
                             normalization.method = "LogNormalize")
sobj = Seurat::FindVariableFeatures(sobj, nfeatures = 2000)
sobj = Seurat::ScaleData(sobj)

sobj

## An object of class Seurat 
## 15121 features across 2329 samples within 1 assay 
## Active assay: RNA (15121 features, 2000 variable features)

We perform a PCA :

sobj = Seurat::RunPCA(sobj,
                      assay = "RNA",
                      reduction.name = "RNA_pca",
                      npcs = 100,
                      seed.use = 1337L)
sobj

## An object of class Seurat 
## 15121 features across 2329 samples within 1 assay 
## Active assay: RNA (15121 features, 2000 variable features)
##  1 dimensional reduction calculated: RNA_pca

We choose the number of dimensions such that they summarize 35 % of the variability :

stdev = sobj@reductions[["RNA_pca"]]@stdev
stdev_prop = cumsum(stdev)/sum(stdev)
ndims = which(stdev_prop > 0.35)[1]
ndims

## [1] 20

We can visualize this on the elbow plot :

elbow_p = Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca") +
  ggplot2::geom_point(x = ndims, y = stdev[ndims], col = "red")
x_text = ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>% as.numeric()
elbow_p = elbow_p +
  ggplot2::scale_x_continuous(breaks = sort(c(x_text, ndims)), limits = c(0, 100))
x_color = ifelse(ggplot_build(elbow_p)$layout$panel_params[[1]]$x$get_labels() %>%
                   as.numeric() %>% round(., 2) == round(ndims, 2), "red", "black")
elbow_p = elbow_p +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.x = element_text(color = x_color))

elbow_p

Without correction

We generate a tSNE and a UMAP with 20 principal components :

sobj = Seurat::RunTSNE(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       reduction.name = paste0("RNA_pca_", ndims, "_tsne"))

sobj = Seurat::RunUMAP(sobj,
                       reduction = "RNA_pca",
                       dims = 1:ndims,
                       seed.use = 1337L,
                       reduction.name = paste0("RNA_pca_", ndims, "_umap"))

We can visualize the two representations :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_tsne")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - tSNE") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("RNA_pca_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

Harmony

We remove batch-effect using Harmony :

`%||%` = function(lhs, rhs) {
  if (!is.null(x = lhs)) {
    return(lhs)
  } else {
    return(rhs)
  }
}

set.seed(1337L)
sobj = harmony::RunHarmony(object = sobj,
                           group.by.vars = "project_name",
                           plot_convergence = TRUE,
                           reduction = "RNA_pca",
                           assay.use = "RNA",
                           reduction.save = "harmony",
                           max.iter.harmony = 20,
                           project.dim = FALSE)

From this batch-effect removed projection, we generate a tSNE and a UMAP.

sobj = Seurat::RunUMAP(sobj, 
                       seed.use = 1337L,
                       dims = 1:ndims,
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_umap"),
                       reduction.key = paste0("harmony_", ndims, "umap_"))
sobj = Seurat::RunTSNE(sobj,
                       dims = 1:ndims,
                       seed.use = 1337L,
                       reduction = "harmony",
                       reduction.name = paste0("harmony_", ndims, "_tsne"),
                       reduction.key = paste0("harmony", ndims, "tsne_"))

These are the corrected UMAP and tSNE :

tsne = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_tsne")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - tSNE") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5),
                 legend.position = "none")

umap = Seurat::DimPlot(sobj, group.by = "project_name",
                       reduction = paste0("harmony_", ndims, "_umap")) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  Seurat::NoAxes() + ggplot2::ggtitle("PCA - harmony - UMAP") +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

tsne | umap

We will keep the tSNE from Harmony :

reduction = "harmony"
name2D = paste0("harmony_", ndims, "_tsne")

Clustering

We generate a clustering :

sobj = Seurat::FindNeighbors(sobj, reduction = reduction, dims = 1:ndims)
sobj = Seurat::FindClusters(sobj, resolution = 1.2)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 2329
## Number of edges: 88685
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7861
## Number of communities: 15
## Elapsed time: 0 seconds

dimplot_clusters = Seurat::DimPlot(sobj, reduction = name2D, label = TRUE) +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1)
dimplot_clusters

Visualization

We can represent the 4 quality metrics :

plot_list = Seurat::FeaturePlot(sobj, reduction = name2D,
                                combine = FALSE, pt.size = 0.5,
                                features = c("percent.mt", "percent.rb", "nFeature_RNA", "log_nCount_RNA"))
plot_list = lapply(plot_list, FUN = function(one_plot) {
  one_plot +
    Seurat::NoAxes() +
    ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
    ggplot2::theme(aspect.ratio = 1)
})

patchwork::wrap_plots(plot_list, nrow = 1)

Project name

We can visualize the two batch-effect corrected representations :

plot_list = lapply(c(paste0("harmony_", ndims, "_tsne"),
                     paste0("harmony_", ndims, "_umap")), FUN = function(one_proj) {
                       Seurat::DimPlot(sobj, group.by = "project_name",
                                       reduction = one_proj) +
                         ggplot2::scale_color_manual(values = sample_info$color,
                                                     breaks = sample_info$project_name) +
                         Seurat::NoAxes() + ggplot2::ggtitle(one_proj) +
                         ggplot2::theme(aspect.ratio = 1,
                                        plot.title = element_text(hjust = 0.5),
                                        legend.position = "none")
                     })

patchwork::wrap_plots(plot_list, ncol = 2)

Cell type

We also visualize cell types :

plot_list = lapply(c(paste0("harmony_", ndims, "_tsne"),
                     paste0("harmony_", ndims, "_umap")), FUN = function(one_proj) {
                       Seurat::DimPlot(sobj, group.by = "cell_type",
                                       reduction = one_proj) +
                         ggplot2::scale_color_manual(values = color_markers,
                                                     breaks = names(color_markers)) +
                         Seurat::NoAxes() + ggplot2::ggtitle(one_proj) +
                         ggplot2::theme(aspect.ratio = 1,
                                        plot.title = element_text(hjust = 0.5),
                                        legend.position = "none")
                     })

patchwork::wrap_plots(plot_list, ncol = 2)

Clusters

We can represent clusters, split by sample of origin :

plot_list = aquarius::plot_split_dimred(sobj,
                                        reduction = name2D,
                                        split_by = "sample_identifier",
                                        group_by = "seurat_clusters",
                                        split_color = setNames(sample_info$color,
                                                               nm = sample_info$sample_identifier),
                                        group_color = aquarius::gg_color_hue(length(levels(sobj$seurat_clusters))),
                                        main_pt_size = 0.5, bg_pt_size = 0.5)

plot_list[[length(plot_list) + 1]] = dimplot_clusters

patchwork::wrap_plots(plot_list, ncol = 4) +
  patchwork::plot_layout(guides = "collect") &
  ggplot2::theme(legend.position = "none")

We make a heatmap to see clusters distribution among samples :

cluster_markers = c("PTPRC",
                    # T cells
                    "CD3E", "CD8A", "CD4", "FOXP3", 
                    "TNFRSF18","KLRD1", "CCL5", "GZMK",
                    # B cells
                    "CD79B",
                    # Macrophages
                    "AIF1", "TREM2", "S100A9", "CLEC10A", "CD209",
                    # Langerhans cells
                    "CD207", "CD1A",
                    # QC metrics
                    "TOP2A", "MCM5",
                    "percent.mt", "percent.rb", "log_nCount_RNA")

ht_annot = Seurat::FetchData(sobj, slot = "data", vars = cluster_markers) %>%
  as.data.frame()
ht_annot$clusters = sobj$seurat_clusters
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))
head(ht_annot)

##      PTPRC       CD3E        CD8A        CD4      FOXP3    TNFRSF18       KLRD1
## 1 2.543856 1.82918786 0.035391287 0.53982329 0.00799106 0.512405370 0.013305988
## 2 2.427109 1.60025420 0.547870134 0.01949895 0.00000000 0.245222230 0.014380917
## 3 2.130124 0.00000000 0.003324244 0.86999731 0.00000000 0.005088733 0.006336963
## 4 2.364368 0.01221124 0.011275448 0.75513061 0.00000000 0.000000000 0.043393065
## 5 2.423070 1.14858059 0.043672883 0.20853118 0.01558572 0.239522293 0.037577573
## 6 2.522189 1.50035535 0.847217508 0.01457912 0.00000000 0.852873029 1.174814857
##         CCL5        GZMK        CD79B        AIF1       TREM2     S100A9
## 1 0.23357833 0.006214020 0.1413334715 0.009376202 0.000000000 0.12342918
## 2 0.31055860 0.005771249 0.1921898488 0.015558414 0.000000000 0.12952068
## 3 0.03661435 0.000000000 0.0009140419 3.160772535 1.478084485 0.10575077
## 4 0.00000000 0.000000000 0.0000000000 2.027052839 0.008062852 0.06189273
## 5 0.20949007 0.103748370 0.1392168466 0.022755603 0.000000000 0.11222624
## 6 2.31571578 0.304641291 0.0898175097 0.013644162 0.000000000 0.11844335
##      CLEC10A      CD209       CD207        CD1A       TOP2A       MCM5
## 1 0.00000000 0.00000000 0.017007320 0.001337840 0.042464521 0.06418254
## 2 0.00000000 0.00000000 0.011623146 0.000000000 0.009536563 0.08137508
## 3 0.03606688 0.03199016 0.239937609 0.005118702 0.020757100 0.33819677
## 4 0.29088339 0.00000000 2.810214031 2.375312583 0.063997046 0.24776597
## 5 0.00000000 0.00000000 0.000000000 0.013035127 0.000000000 0.05266395
## 6 0.00000000 0.00000000 0.008067921 0.000000000 0.000000000 0.03665464
##   percent.mt percent.rb log_nCount_RNA
## 1   3.139178   20.83574       8.186721
## 2   4.242166   20.25231       8.047249
## 3   4.669895   13.67333       9.434672
## 4   6.176933   12.12156       8.441054
## 5   8.742247   14.83950       7.589494
## 6   4.204088   21.50509       8.507769

color_fun = function(one_gene) {
  gene_range = range(ht_annot[, one_gene])
  gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
                                      breaks = seq(from = gene_range[1], to = gene_range[2],
                                                   length.out = length(aquarius::color_gene)))
  return(gene_palette)
}

ha = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                       which = "column",
                                       show_legend = TRUE,
                                       col = setNames(nm = cluster_markers,
                                                      lapply(cluster_markers, FUN = color_fun)),
                                       annotation_name_side = "left")

ht = aquarius::plot_prop_heatmap(df = sobj@meta.data[, c("sample_identifier", "seurat_clusters")],
                                 bottom_annotation = ha,
                                 cluster_rows = TRUE,
                                 prop_margin = 1,
                                 row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                           col = sample_info$color,
                                                           fontface = "bold"),
                                 row_title = "Sample",
                                 column_title = "Cluster")

ComplexHeatmap::draw(ht,
                     merge_legends = TRUE)

We also look at genes of interest on the projection :

plot_list = lapply(cluster_markers, FUN = function(one_gene) {
  p = Seurat::FeaturePlot(sobj, features = one_gene,
                          pt.size = 0.2, reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
    Seurat::NoAxes() +
    ggplot2::theme(aspect.ratio = 1)
  
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 5)

Save

We save the Seurat object :

saveRDS(sobj, file = paste0(out_dir, "/", save_name, "_sobj.rds"))

R Session

show

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.3.5   patchwork_1.1.2 dplyr_1.0.7    
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 vctrs_0.3.8                
##   [7] usethis_2.0.1               dynwrap_1.2.1              
##   [9] blob_1.2.1                  survival_3.2-13            
##  [11] prodlim_2019.11.13          dynutils_1.0.5             
##  [13] later_1.3.0                 DBI_1.1.1                  
##  [15] R.utils_2.11.0              SingleCellExperiment_1.8.0 
##  [17] rappdirs_0.3.3              uwot_0.1.8                 
##  [19] dqrng_0.2.1                 jpeg_0.1-8.1               
##  [21] zlibbioc_1.32.0             pspline_1.0-18             
##  [23] pcaMethods_1.78.0           mvtnorm_1.1-1              
##  [25] htmlwidgets_1.5.4           GlobalOptions_0.1.2        
##  [27] future_1.22.1               UpSetR_1.4.0               
##  [29] laeken_0.5.2                leiden_0.3.3               
##  [31] clustree_0.4.3              parallel_3.6.3             
##  [33] scater_1.14.6               irlba_2.3.3                
##  [35] DEoptimR_1.0-9              tidygraph_1.1.2            
##  [37] Rcpp_1.0.9                  readr_2.0.2                
##  [39] KernSmooth_2.23-17          carrier_0.1.0              
##  [41] promises_1.1.0              gdata_2.18.0               
##  [43] DelayedArray_0.12.3         limma_3.42.2               
##  [45] graph_1.64.0                RcppParallel_5.1.4         
##  [47] Hmisc_4.4-0                 fs_1.5.2                   
##  [49] RSpectra_0.16-0             fastmatch_1.1-0            
##  [51] ranger_0.12.1               digest_0.6.25              
##  [53] png_0.1-7                   sctransform_0.2.1          
##  [55] cowplot_1.0.0               DOSE_3.12.0                
##  [57] here_1.0.1                  TInGa_0.0.0.9000           
##  [59] ggraph_2.0.3                pkgconfig_2.0.3            
##  [61] GO.db_3.10.0                DelayedMatrixStats_1.8.0   
##  [63] gower_0.2.1                 ggbeeswarm_0.6.0           
##  [65] iterators_1.0.12            DropletUtils_1.6.1         
##  [67] reticulate_1.26             clusterProfiler_3.14.3     
##  [69] SummarizedExperiment_1.16.1 circlize_0.4.15            
##  [71] beeswarm_0.4.0              GetoptLong_1.0.5           
##  [73] xfun_0.35                   bslib_0.3.1                
##  [75] zoo_1.8-10                  tidyselect_1.1.0           
##  [77] reshape2_1.4.4              purrr_0.3.4                
##  [79] ica_1.0-2                   pcaPP_1.9-73               
##  [81] viridisLite_0.3.0           rtracklayer_1.46.0         
##  [83] rlang_1.0.2                 hexbin_1.28.1              
##  [85] jquerylib_0.1.4             dyneval_0.9.9              
##  [87] glue_1.4.2                  RColorBrewer_1.1-2         
##  [89] matrixStats_0.56.0          stringr_1.4.0              
##  [91] lava_1.6.7                  europepmc_0.3              
##  [93] DESeq2_1.26.0               recipes_0.1.17             
##  [95] labeling_0.3                harmony_0.1.0              
##  [97] httpuv_1.5.2                class_7.3-17               
##  [99] BiocNeighbors_1.4.2         DO.db_2.9                  
## [101] annotate_1.64.0             jsonlite_1.7.2             
## [103] XVector_0.26.0              bit_4.0.4                  
## [105] mime_0.9                    aquarius_0.1.5             
## [107] Rsamtools_2.2.3             gridExtra_2.3              
## [109] gplots_3.0.3                stringi_1.4.6              
## [111] processx_3.5.2              gsl_2.1-6                  
## [113] bitops_1.0-6                cli_3.0.1                  
## [115] batchelor_1.2.4             RSQLite_2.2.0              
## [117] randomForest_4.6-14         tidyr_1.1.4                
## [119] data.table_1.14.2           rstudioapi_0.13            
## [121] org.Mm.eg.db_3.10.0         GenomicAlignments_1.22.1   
## [123] nlme_3.1-147                qvalue_2.18.0              
## [125] scran_1.14.6                locfit_1.5-9.4             
## [127] scDblFinder_1.1.8           listenv_0.8.0              
## [129] ggthemes_4.2.4              gridGraphics_0.5-0         
## [131] R.oo_1.24.0                 dbplyr_1.4.4               
## [133] BiocGenerics_0.32.0         TTR_0.24.2                 
## [135] readxl_1.3.1                lifecycle_1.0.1            
## [137] timeDate_3043.102           ggpattern_0.3.1            
## [139] munsell_0.5.0               cellranger_1.1.0           
## [141] R.methodsS3_1.8.1           proxyC_0.1.5               
## [143] visNetwork_2.0.9            caTools_1.18.0             
## [145] codetools_0.2-16            Biobase_2.46.0             
## [147] GenomeInfoDb_1.22.1         vipor_0.4.5                
## [149] lmtest_0.9-38               msigdbr_7.5.1              
## [151] htmlTable_1.13.3            triebeard_0.3.0            
## [153] lsei_1.2-0                  xtable_1.8-4               
## [155] ROCR_1.0-7                  BiocManager_1.30.10        
## [157] scatterplot3d_0.3-41        abind_1.4-5                
## [159] farver_2.0.3                parallelly_1.28.1          
## [161] RANN_2.6.1                  askpass_1.1                
## [163] GenomicRanges_1.38.0        RcppAnnoy_0.0.16           
## [165] tibble_3.1.5                ggdendro_0.1-20            
## [167] cluster_2.1.0               future.apply_1.5.0         
## [169] Seurat_3.1.5                dendextend_1.15.1          
## [171] Matrix_1.3-2                ellipsis_0.3.2             
## [173] prettyunits_1.1.1           lubridate_1.7.9            
## [175] ggridges_0.5.2              igraph_1.2.5               
## [177] RcppEigen_0.3.3.7.0         fgsea_1.12.0               
## [179] remotes_2.4.2               scBFA_1.0.0                
## [181] destiny_3.0.1               VIM_6.1.1                  
## [183] testthat_3.1.0              htmltools_0.5.2            
## [185] BiocFileCache_1.10.2        yaml_2.2.1                 
## [187] utf8_1.1.4                  plotly_4.9.2.1             
## [189] XML_3.99-0.3                ModelMetrics_1.2.2.2       
## [191] e1071_1.7-3                 foreign_0.8-76             
## [193] withr_2.5.0                 fitdistrplus_1.0-14        
## [195] BiocParallel_1.20.1         xgboost_1.4.1.1            
## [197] bit64_4.0.5                 foreach_1.5.0              
## [199] robustbase_0.93-9           Biostrings_2.54.0          
## [201] GOSemSim_2.13.1             rsvd_1.0.3                 
## [203] memoise_2.0.0               evaluate_0.18              
## [205] forcats_0.5.0               rio_0.5.16                 
## [207] geneplotter_1.64.0          tzdb_0.1.2                 
## [209] caret_6.0-86                ps_1.6.0                   
## [211] DiagrammeR_1.0.6.1          curl_4.3                   
## [213] fdrtool_1.2.15              fansi_0.4.1                
## [215] highr_0.8                   urltools_1.7.3             
## [217] xts_0.12.1                  GSEABase_1.48.0            
## [219] acepack_1.4.1               edgeR_3.28.1               
## [221] checkmate_2.0.0             scds_1.2.0                 
## [223] cachem_1.0.6                npsurv_0.4-0               
## [225] babelgene_22.3              rjson_0.2.20               
## [227] openxlsx_4.1.5              ggrepel_0.9.1              
## [229] clue_0.3-60                 rprojroot_2.0.2            
## [231] stabledist_0.7-1            tools_3.6.3                
## [233] sass_0.4.0                  nichenetr_1.1.1            
## [235] magrittr_2.0.1              RCurl_1.98-1.2             
## [237] proxy_0.4-24                car_3.0-11                 
## [239] ape_5.3                     ggplotify_0.0.5            
## [241] xml2_1.3.2                  httr_1.4.2                 
## [243] assertthat_0.2.1            rmarkdown_2.18             
## [245] boot_1.3-25                 globals_0.14.0             
## [247] R6_2.4.1                    Rhdf5lib_1.8.0             
## [249] nnet_7.3-14                 RcppHNSW_0.2.0             
## [251] progress_1.2.2              genefilter_1.68.0          
## [253] statmod_1.4.34              gtools_3.8.2               
## [255] shape_1.4.6                 HDF5Array_1.14.4           
## [257] BiocSingular_1.2.2          rhdf5_2.30.1               
## [259] splines_3.6.3               AUCell_1.8.0               
## [261] carData_3.0-4               colorspace_1.4-1           
## [263] generics_0.1.0              stats4_3.6.3               
## [265] base64enc_0.1-3             dynfeature_1.0.0           
## [267] smoother_1.1                gridtext_0.1.1             
## [269] pillar_1.6.3                tweenr_1.0.1               
## [271] sp_1.4-1                    ggplot.multistats_1.0.0    
## [273] rvcheck_0.1.8               GenomeInfoDbData_1.2.2     
## [275] plyr_1.8.6                  gtable_0.3.0               
## [277] zip_2.2.0                   knitr_1.41                 
## [279] ComplexHeatmap_2.14.0       latticeExtra_0.6-29        
## [281] biomaRt_2.42.1              IRanges_2.20.2             
## [283] fastmap_1.1.0               ADGofTest_0.3              
## [285] copula_1.0-0                doParallel_1.0.15          
## [287] AnnotationDbi_1.48.0        vcd_1.4-8                  
## [289] babelwhale_1.0.1            openssl_1.4.1              
## [291] scales_1.1.1                backports_1.2.1            
## [293] S4Vectors_0.24.4            ipred_0.9-12               
## [295] enrichplot_1.6.1            hms_1.1.1                  
## [297] ggforce_0.3.1               Rtsne_0.15                 
## [299] shiny_1.7.1                 numDeriv_2016.8-1.1        
## [301] polyclip_1.10-0             grid_3.6.3                 
## [303] lazyeval_0.2.2              Formula_1.2-3              
## [305] tsne_0.1-3                  crayon_1.3.4               
## [307] MASS_7.3-54                 pROC_1.16.2                
## [309] viridis_0.5.1               dynparam_1.0.0             
## [311] rpart_4.1-15                zinbwave_1.8.0             
## [313] compiler_3.6.3              ggtext_0.1.0